The Experts below are selected from a list of 81 Experts worldwide ranked by ideXlab platform
Zhe-ming Lu - One of the best experts on this subject based on the ideXlab platform.
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Lossless Information Hiding in Block Truncation Coding–Compressed Images
Lossless Information Hiding in Images, 2020Co-Authors: Zhe-ming LuAbstract:This chapter focuses on block truncation coding (BTC)–based lossless information hiding schemes. BTC [1] is a block-based spatial domain image compression technique for grayscale images. Its main idea is to quantize the pixels in each block into two levels while preserving certain statistical moments of small blocks of the grayscale image. The absolute moment BTC [2] was proposed as a special kind of BTC to preserve the Mean and the first absolute central moment of a block. After BTC compression, each block can be represented by a bitplane together with a pair of Means called higher Mean and lower Mean. All higher Means comprise a higher Mean Table, whereas all lower Means comprise a lower Mean Table. To embed information, we can modify either the BTC encoding stage or the BTC-compressed data according to the secret bits. For reversible information hiding, we commonly embed information in BTC-compressed data, i.e., we can embed data in bitplanes, or Mean Tables, or both.
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High performance reversible data hiding for block truncation coding compressed images
Signal Image and Video Processing, 2011Co-Authors: Zhe-ming Lu, Fa-xin Yu, Rong-jun ShenAbstract:Reversible data hiding has been a hot research topic because it can recover both the host media and hidden data without distortion. Because most digital images are stored and transmitted in compressed forms, such as JPEG, vector quantization, and block truncation coding (BTC), the reversible data hiding schemes in compressed domains have been paid more and more attention. Compared with transform coding, BTC has a significantly low complexity and less memory requirement, it therefore becomes an ideal data hiding domain. Traditional data hiding schemes in the BTC domain modify the BTC encoding stage or BTC-compressed data according to the secret bits, and they have a relatively low efficiency and Meanwhile may reduce the image quality. This paper presents a novel reversible data hiding scheme based on the joint neighbor coding technique for BTC-compressed images by further losslessly encoding the BTC-compressed data according to the secret bits. First, BTC is performed on the original image to obtain the BTC-compressed data that can be represented by a high Mean Table, a low Mean Table, and a bitplane sequence. Then, the secret data are losslessly embedded in both the high Mean and low Mean Tables. Our hiding scheme is a lossless method based on the relation among the current value and the neighboring ones in Mean Tables. In addition, it can averagely embed 2 bits in each Mean value, which increases the capacity and efficiency. Experimental results show that our scheme outperforms three existing BTC-based data hiding works, in terms of the bit rate, capacity, and efficiency.
Rong-jun Shen - One of the best experts on this subject based on the ideXlab platform.
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High performance reversible data hiding for block truncation coding compressed images
Signal Image and Video Processing, 2011Co-Authors: Zhe-ming Lu, Fa-xin Yu, Rong-jun ShenAbstract:Reversible data hiding has been a hot research topic because it can recover both the host media and hidden data without distortion. Because most digital images are stored and transmitted in compressed forms, such as JPEG, vector quantization, and block truncation coding (BTC), the reversible data hiding schemes in compressed domains have been paid more and more attention. Compared with transform coding, BTC has a significantly low complexity and less memory requirement, it therefore becomes an ideal data hiding domain. Traditional data hiding schemes in the BTC domain modify the BTC encoding stage or BTC-compressed data according to the secret bits, and they have a relatively low efficiency and Meanwhile may reduce the image quality. This paper presents a novel reversible data hiding scheme based on the joint neighbor coding technique for BTC-compressed images by further losslessly encoding the BTC-compressed data according to the secret bits. First, BTC is performed on the original image to obtain the BTC-compressed data that can be represented by a high Mean Table, a low Mean Table, and a bitplane sequence. Then, the secret data are losslessly embedded in both the high Mean and low Mean Tables. Our hiding scheme is a lossless method based on the relation among the current value and the neighboring ones in Mean Tables. In addition, it can averagely embed 2 bits in each Mean value, which increases the capacity and efficiency. Experimental results show that our scheme outperforms three existing BTC-based data hiding works, in terms of the bit rate, capacity, and efficiency.
Ashutosh Aggarwal - One of the best experts on this subject based on the ideXlab platform.
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Hiding clinical information in medical images: an enhanced encrypted reversible data hiding algorithm grounded on hierarchical absolute moment block truncation coding
Multidimensional Systems and Signal Processing, 2020Co-Authors: Rupali Bhardwaj, Ashutosh AggarwalAbstract:Reversible data hiding in the encrypted domain has attracted a lot of consideration because of the necessity for content security and protection assurance. Majority of the prior reversible data hiding algorithms are designed for absolute moment block truncation coding (AMBTC) compressed images. In this paper, an enhanced encrypted reversible data hiding algorithm grounded on hierarchical AMBTC has been proposed. In the proposed algorithm, high Mean Table, low Mean Table and bitmap sequence Table obtained through hierarchical AMBTC are first encrypted using homomorphic cryptosystem and then a secret ternary data is embedded in each grey pixel of high Mean Table, low Mean Table and bitmap sequence Table (except zero (0) value) without any underflow or overflow issue. Exhaustive experiments have been performed on natural and medical test images which demonstrates the superiority of the proposed algorithm over the existing reversible data hiding algorithms. Experimental study reveals that the proposed algorithm has more payload with superior image quality than the existing algorithms.
Fa-xin Yu - One of the best experts on this subject based on the ideXlab platform.
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High performance reversible data hiding for block truncation coding compressed images
Signal Image and Video Processing, 2011Co-Authors: Zhe-ming Lu, Fa-xin Yu, Rong-jun ShenAbstract:Reversible data hiding has been a hot research topic because it can recover both the host media and hidden data without distortion. Because most digital images are stored and transmitted in compressed forms, such as JPEG, vector quantization, and block truncation coding (BTC), the reversible data hiding schemes in compressed domains have been paid more and more attention. Compared with transform coding, BTC has a significantly low complexity and less memory requirement, it therefore becomes an ideal data hiding domain. Traditional data hiding schemes in the BTC domain modify the BTC encoding stage or BTC-compressed data according to the secret bits, and they have a relatively low efficiency and Meanwhile may reduce the image quality. This paper presents a novel reversible data hiding scheme based on the joint neighbor coding technique for BTC-compressed images by further losslessly encoding the BTC-compressed data according to the secret bits. First, BTC is performed on the original image to obtain the BTC-compressed data that can be represented by a high Mean Table, a low Mean Table, and a bitplane sequence. Then, the secret data are losslessly embedded in both the high Mean and low Mean Tables. Our hiding scheme is a lossless method based on the relation among the current value and the neighboring ones in Mean Tables. In addition, it can averagely embed 2 bits in each Mean value, which increases the capacity and efficiency. Experimental results show that our scheme outperforms three existing BTC-based data hiding works, in terms of the bit rate, capacity, and efficiency.
Rupali Bhardwaj - One of the best experts on this subject based on the ideXlab platform.
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Hiding clinical information in medical images: an enhanced encrypted reversible data hiding algorithm grounded on hierarchical absolute moment block truncation coding
Multidimensional Systems and Signal Processing, 2020Co-Authors: Rupali Bhardwaj, Ashutosh AggarwalAbstract:Reversible data hiding in the encrypted domain has attracted a lot of consideration because of the necessity for content security and protection assurance. Majority of the prior reversible data hiding algorithms are designed for absolute moment block truncation coding (AMBTC) compressed images. In this paper, an enhanced encrypted reversible data hiding algorithm grounded on hierarchical AMBTC has been proposed. In the proposed algorithm, high Mean Table, low Mean Table and bitmap sequence Table obtained through hierarchical AMBTC are first encrypted using homomorphic cryptosystem and then a secret ternary data is embedded in each grey pixel of high Mean Table, low Mean Table and bitmap sequence Table (except zero (0) value) without any underflow or overflow issue. Exhaustive experiments have been performed on natural and medical test images which demonstrates the superiority of the proposed algorithm over the existing reversible data hiding algorithms. Experimental study reveals that the proposed algorithm has more payload with superior image quality than the existing algorithms.